SpectralUnmix: A Torch-Based Regularized Non-negative Matrix Factorization

We present SpectralUnmix, an R package for regularized non-negative matrix factorization (NMF), implemented in torch with optional GPU acceleration. The package estimates low-rank non-negative representations through proximal-gradient updates and allows smoothness regularization along the spectral axis. As a compact demonstration, we apply the method to a subset of stellar spectra and compare the recovered NMF components with principal-component directions and representative stellar spectra. The package is released under the MIT license at https://rafaelsdesouza.github.io/SpectralUnmix/; a copy has been deposited to Zenodo (R. S. de Souza 2026).